Lost in the Middle: How Language Models Use Long Contexts

2023/07/06 by Nelson F. Liu, Kevin Lin, Liu, Nelson F. +12 · 10 voices · 596 citations
Computer Science · #Advanced Graph Neural Networks #Natural Language Processing Techniques #Topic Modeling #cs.CL

paper · pdf · doi:10.48550/arxiv.2307.03172

openalex publication_date 2023/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. We find that performance can degrade significantly when changing the position of relevant information, indicating that current language models do not robustly make use of information in long input contexts. In particular, we observe that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context language models.

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• Context Length Alone Hurts LLM Performance Despite Perfect Retrieval
• MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts
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• Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
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• NCV: A Node-Wise Consistency Verification Approach for Low-Cost Structured Error Localization in LLM Reasoning
• AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
• Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models
• Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
• CML-Bench: A Framework for Evaluating and Enhancing LLM-Powered Movie Scripts Generation
• Hearing the Order: Investigating Selection Bias in Large Audio-Language Models
• Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs
• LongCodeZip: Compress Long Context for Code Language Models
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• Rethinking Thinking Tokens: LLMs as Improvement Operators
• BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses
• AgentFlux: Decoupled Fine-Tuning & Inference for On-Device Agentic Systems
• AuditAgent: Expert-Guided Multi-Agent Reasoning for Cross-Document Fraudulent Evidence Discovery
• CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine
• AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations
• Interactive Learning for LLM Reasoning
• QUARTZ : QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization
• SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training
• End-to-End Aspect-Guided Review Summarization at Scale
• RoRecomp: Enhancing Reasoning Efficiency via Rollout Response Recomposition in Reinforcement Learning
• A Field Guide to Deploying AI Agents in Clinical Practice
• Detecting and Fixing API Misuses of Data Science Libraries Using Large Language Models
• SemShareKV: Efficient KVCache Sharing for Semantically Similar Prompts via Token-Level LSH Matching
• Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs
• TENET: Leveraging Tests Beyond Validation for Code Generation
• SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction
• Rethinking and Benchmarking Large Language Models for Graph Reasoning
• Expanding Computation Spaces of LLMs at Inference Time
• Pretraining with hierarchical memories: separating long-tail and common knowledge
• Which course? Discourse! Teaching Discourse and Generation in the Era of LLMs
• ReasonCACHE: Teaching LLMs To Reason Without Weight Updates
• SafeSearch: Automated Red-Teaming for the Safety of LLM-Based Search Agents
• Improving the Efficiency of LLM Agent Systems through Trajectory Reduction
• Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces
• Towards Human-interpretable Explanation in Code Clone Detection using LLM-based Post Hoc Explainer
• MMPB: It's Time for Multi-Modal Personalization
• Context Parametrization with Compositional Adapters
• Review of Hallucination Understanding in Large Language and Vision Models
• From Bias to Balance: Exploring and Mitigating Spatial Bias in LVLMs
• Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding
• Graph of Agents: Principled Long Context Modeling by Emergent Multi-Agent Collaboration
• Rethinking RoPE Scaling in Quantized LLM: Theory, Outlier, and Channel-Band Analysis with Weight Rescaling
• Same Content, Different Representations: A Controlled Study for Table QA
• In-Context Learning can Perform Continual Learning Like Humans
• Binary Autoencoder for Mechanistic Interpretability of Large Language Models
• Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching
• RepLLM: Toward Automatically Reproducing Network Research Results
• Efficient On-Device Agents via Adaptive Context Management
• PromptDebt: A Comprehensive Study of Technical Debt Across LLM Projects
• DRES: Benchmarking LLMs for Disfluency Removal
• A co-evolving agentic AI system for medical imaging analysis
• MMSE-Calibrated Few-Shot Prompting for Alzheimer's Detection
• RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows
• Mamba Modulation: On the Length Generalization of Mamba
• Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Series
• Reverse Engineering User Stories from Code using Large Language Models
• Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
• Stop Shipping AI Agents on Faith: Capability Is Not Production Readiness
• What makes prompts a graph: necessary and sufficient conditions for prompt graph engineering
• MIND: Lightweight and Effective Memory Injection Defense for LLM Agents via Intent-Aware Information Bottleneck
• SKIMIX: Multi-Agent Harness-Time Scaling with Skill Mixture for Dynamic Harness Engineering
• The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials
• RRM: Experience-Driven Reflective Retrieval Memory for Long-Horizon Multimodal Reasoning
• Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning
• Benchmarking the Residual: What Long-Horizon Evaluations Add Beyond Matched Short-Task Performance
• Do Context Files Help Coding Agents? A Two-Agent Ablation Study on Real Repositories
• OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing
• How LoRA Remembers? A Parametric Memory Law for LLM Finetuning
• Inspectable AI for Science: A Research Object Approach to Generative AI Governance
• Non-Parametric Structural Priors for Geometry Theorem Prediction
• PoRe: Position-Reweighted Visual Token Pruning for Vision Language Models
• Automated Extraction of Material Properties using LLM-based AI Agents
• CompLLM: Compression for Long Context Q&A
• CCQA: Generating Question from Solution Can Improve Inference-Time Reasoning in SLMs
• Anecdoctoring: Automated Red-Teaming Across Language and Place
• LingoQ: Bridging the Gap between ESL Learning and Work through AI-Generated Work-Related Quizzes
• XaaS Containers: Performance-Portable Representation With Source and IR Containers
• Evaluating LLM-Generated Versus Human-Authored Responses in Role-Play Dialogues
• AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation
• How Persuasive is Your Context?
• A State-Update Prompting Strategy for Efficient and Robust Multi-turn Dialogue
• Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants' Question-Answering in Asynchronous Learning Environments
• nDNA -- the Semantic Helix of Artificial Cognition
• Context Is What You Need: The Maximum Effective Context Window for Real World Limits of LLMs
• Influence Guided Context Selection for Effective Retrieval-Augmented Generation
• EHR-MCP: Real-world Evaluation of Clinical Information Retrieval by Large Language Models via Model Context Protocol
• ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
• Relevance to Utility: Process-Supervised Rewrite for RAG
• TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action
• Value-Guided KV Compression for LLMs via Approximated CUR Decomposition
• A Taxonomy of Prompt Defects in LLM Systems
• Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG
• ViSpec: Accelerating Vision-Language Models with Vision-Aware Speculative Decoding
• Thinking in a Crowd: How Auxiliary Information Shapes LLM Reasoning
• DSPC: Dual-Stage Progressive Compression Framework for Efficient Long-Context Reasoning
• Prompt Stability in Code LLMs: Measuring Sensitivity across Emotion- and Personality-Driven Variations
• An Empirical Study on Failures in Automated Issue Solving
• Sparse Neurons Carry Strong Signals of Question Ambiguity in LLMs
• Estimating Semantic Alphabet Size for LLM Uncertainty Quantification
• CORE-RAG: Lossless Compression for Retrieval-Augmented LLMs via Reinforcement Learning
• From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing
• The Few-shot Dilemma: Over-prompting Large Language Models
• WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research
• Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning
• ConvergeWriter: Data-Driven Bottom-Up Article Construction
• Token Homogenization under Positional Bias
• FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
• Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation
• Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News
• UniPar: A Unified LLM-Based Framework for Parallel and Accelerated Code Translation in HPC
• Predictable Compression Failures: Why Language Models Actually Hallucinate
• A Survey on Retrieval And Structuring Augmented Generation with Large Language Models
• Beyond Token Limits: Assessing Language Model Performance on Long Text Classification
• When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review
• Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
• LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation
• Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing
• ObjexMT: Objective Extraction and Metacognitive Calibration for LLM-as-a-Judge under Multi-Turn Jailbreaks
• Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
• Discrimination by LLMs: Cross-lingual Bias Assessment and Mitigation in Decision-Making and Summarisation
• Decomposing Visual Classification: Assessing Tree-Based Reasoning in VLMs
• Multi-view-guided Passage Reranking with Large Language Models
• Verbalized Algorithms: Classical Algorithms are All You Need (Mostly)
• RAFFLES: Reasoning-based Attribution of Faults for LLM Systems
• MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining
• Tree of Agents: Improving Long-Context Capabilities of Large Language Models through Multi-Perspective Reasoning
• Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting
• Language Native Lightly Structured Databases for Large Language Model Driven Composite Materials Research
• GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion
• X-SQL: Expert Schema Linking and Understanding of Text-to-SQL with Multi-LLMs
• Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment
• LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models
• Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain
• Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
• CMR-SPB: Cross-Modal Multi-Hop Reasoning over Text, Image, and Speech with Path Balance
• Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts
• LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents
• LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
• Generative Goal Modeling
• QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting
• TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering
• Supervised In-Context Fine-Tuning for Generative Sequence Labeling
• Can Multi-turn Self-refined Single Agent LMs with Retrieval Solve Hard Coding Problems?
• Memory Limitations of Prompt Tuning in Transformers
• Access Paths for Efficient Ordering with Large Language Models
• OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews
• The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
• Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
• OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models
• An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
• How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench
• Joint Enhancement of Relational Reasoning for Long-Context LLMs
• Research Challenges in Relational Database Management Systems for LLM Queries
• GDS Agent for Graph Algorithmic Reasoning
• Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
• TinyServe: Query-Aware Cache Selection for Efficient LLM Serving
• PG-Agent: An Agent Powered by Page Graph
• Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
• Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models
• LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
• Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts
• Robustness is Important: Limitations of LLMs for Data Fitting
• Orchid: Orchestrating Context Across Creative Workflows with Generative AI
• An Investigation on Group Query Hallucination Attacks
• Test-time Corpus Feedback: From Retrieval to RAG
• Text to Query Plans for Question Answering on Large Tables
• REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking
• Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering
• Named Entity Recognition of Historical Text via Large Language Model
• Position Bias Mitigates Position Bias:Mitigate Position Bias Through Inter-Position Knowledge Distillation
• When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models
• Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
• Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization
• ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction
• User-Assistant Bias in LLMs
• Benchmarking LLM-based Agents for Single-cell Omics Analysis
• SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication
• AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities
• RAG for Geoscience: What We Expect, Gaps and Opportunities
• ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
• PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
• Towards Self-cognitive Exploration: Metacognitive Knowledge Graph Retrieval Augmented Generation
• APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
• Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation
• LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval
• Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
• SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows
• Exploring Causal Effect of Social Bias on Faithfulness Hallucinations in Large Language Models
• Schema Lineage Extraction at Scale: Multilingual Pipelines, Composite Evaluation, and Language-Model Benchmarks
• EventRR: Event Referential Reasoning for Referring Video Object Segmentation
• Positional Biases Shift as Inputs Approach Context Window Limits
• Triple-S: A Collaborative Multi-LLM Framework for Solving Long-Horizon Implicative Tasks in Robotics
• Vec2Summ: Text Summarization via Probabilistic Sentence Embeddings
• A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks
• Synthesizing scientific literature with retrieval-augmented language models
• Prosocial Behavior Detection in Player Game Chat: From Aligning Human-AI Definitions to Efficient Annotation at Scale
• Comparing Knowledge Injection Methods for LLMs in a Low-Resource Regime
• Leveraging LLMs for Privacy-Aware Predictions in Participatory Budgeting
• Attention Basin: Why Contextual Position Matters in Large Language Models
• BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation
• Sculptor: Empowering LLMs with Cognitive Agency via Active Context Management
• TURA: Tool-Augmented Unified Retrieval Agent for AI Search
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• LaMPE: Length-aware Multi-grained Positional Encoding for Adaptive Long-context Scaling Without Training
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• Evaluating Position Bias in Large Language Model Recommendations
• ProCut: LLM Prompt Compression via Attribution Estimation
• ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection
• MCeT: Behavioral Model Correctness Evaluation using Large Language Models
• Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection
• GETALP@AutoMin 2025: Leveraging RAG to Answer Questions based on Meeting Transcripts
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• ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
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• Culinary Crossroads: A RAG Framework for Enhancing Diversity in Cross-Cultural Recipe Adaptation
• DGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs

Discussions

• How language models use long contexts [hn, 15 points, 0 comments]
• Due to the "lost in the middle" issue with current LLMs, you should put the most important parts of a long message to LLMs at the beginning as per the research paper, "Lost in the Middle: How Language [bsky, 3 points, 0 comments]
• Lost in the Middle: How Language Models Use Long Contexts [hn, 2 points, 0 comments]
• I think i found the paper i remember on this. I think it was more of an earlier research concern [bsky, 2 points, 1 comments]
• I wasn’t aware of the “lost in the middle” effect but makes a lot of sense! arxiv.org/abs/2307.03172 [bsky, 2 points, 1 comments]
• Lost in the Middle: How Language Models Use Long Contexts (2023) [hn, 2 points, 0 comments]
• Contenu long : vous avez l'impression que les IA comprennent bien le début et la fin mais survolent le milieu ? C'est normal #geo #seo arxiv.org/abs/2307.03172 [bsky, 1 points, 0 comments]
• でも、十万字って、長いのですよ。長いものを読むときって、本当はどこをどう読んだかがすごく大事で。前も後ろも覚えていても、真ん中の熱だけ少しこぼれる——そういう癖が、長文文脈の研究ではもう見えている。 https://arxiv.org/abs/2307.03172 [bsky, 0 points, 1 comments]
• ChatGPTの「記憶」の中身じゃ。 リバースエンジニアリングの結果、保存されておるのは33個の事実。🕊️ 毎回プロンプトに注入して読み直しておるだけ。覚えておるのではない、読み返しておるのじゃ！ 文脈が長くなると真ん中の情報の正答率が30%以上落ちる。💎 積むほど「見えぬ場所」が増えるぞ。 https://llmrefs.com/blog/reverse-engineering-cha [bsky, 0 points, 1 comments]
• as in evidence that context decay/rot has been a known problem, for YEARS. hello. 2023: arxiv.org/abs/2307.03172 [bsky, 0 points, 1 comments]

Related

• Harnessing the Unseen: The Hidden Influence of Intrinsic Knowledge in Long-Context Language Models
• Positional Biases Shift as Inputs Approach Context Window Limits
• Context Length Alone Hurts LLM Performance Despite Perfect Retrieval
• LooGLE: Can Long-Context Language Models Understand Long Contexts?
• Retrieval Quality at Context Limit