1. Lost in the Middle — diagnosis and fixes

Why it happens

Models with popular positional schemes (e.g., RoPE and position extrapolation methods) exhibit a positional bias: they usually “retain” the beginning and the end of a sequence better, while the middle is often the weakest. When the needle lands in the middle, accuracy drops — even in “long-context” models.

What works immediately (no training)

1. Extreme ordering: after reranking, place the most important fragments at the beginning and end of context (use the middle for supporting material).
2. Iterative retrieval (Self-RAG/FLARE): don’t load everything at once — generate in steps and pull missing facts on demand.
3. Hierarchical summaries (map → reduce): condense sections to 1–2 sentences and compose a “map” of arguments.

What works at the model/pipeline level

• Cross-encoder reranking (cross-encoder/LLM ranker) before arranging context.
• Better positional handling: LongRoPE/YaRN/PI — more stable windows; and Ms-PoE (Found in the Middle) targeted at improving the “middle.”
• SFT with positional debiasing: rotation/shuffling of order, needles placed at different positions; generally — positional augmentation aligned with the latest long-context guidance.

Prompt pattern (minimal)

TASK: {question}
RULES: Use only fragments marked [IMPORTANT]. Omit [ADDITIONAL].
FORMAT:
[EVIDENCE] bullet points with a citation --> {passage_id}
[ANSWER] concise conclusion

2. Distracting prompts and the “tempting” noise

Why it happens

LLMs are sensitive to distractors that are semantically similar to the correct answer. A single irrelevant sentence can steer reasoning off course.

Tactics without training

1. Instruction + answer format: ask to list evidence/relevant sentences first, then provide the conclusion.
2. Few-shot with distractors: in examples, show the step “filter out irrelevant → solve.”
3. Self-Consistency: several independent reasoning trajectories and majority voting.
4. Passage-level gating/abstention: test each fragment — if it doesn’t contain necessary information, the model should output NO-RESPONSE; apply this filter before final generation.

Noise-robust RAG pipeline

Retriever → Cross-Encoder Rerank → NLI/entailment filters → Compression (LongLLMLingua/LLMLingua-2) → Extreme ordering → Gating/abstention → Generation with self-consistency.

Prompt pattern (passage gating)

For each passage do:
1) Does this passage contain information required to answer "{question}"?
2) If NO, return: NO-RESPONSE | {id}
3) If YES, return: EVIDENCE | {id} | {short_quote}

3. Very large contexts and performance degradation

Where degradation comes from

• Positions: extrapolation beyond the training distribution.
• Optimisation: long sequences strain the KV cache and attention stability.
• Selection: the practically useful portion is smaller than the declared window; the rest often is noise.

Layers of defence (from practice to architecture)

A. Practice “by tomorrow”

• Context compression (LongLLMLingua/LLMLingua-2): 2–6× shorter inputs; in many tasks no loss, sometimes improved quality; reported speedups ~1.4–2.9× depending on the task.
• Adaptive retrieval (Self-RAG/FLARE): “retrieve only when needed,” with self-reflection on sources.
• Positional budget: aim for ~20–40% of the window for hard evidence; leave the rest for the model’s own reasoning (heuristic — A/B test on your data).

B. Model and inference

• LongRoPE/YaRN/PI: stabilisation and extension of 32k+ windows.
• ProLong: continued pre-training + SFT for long sequences (models up to 512k).
• LIFT (test-time adaptation): adapter/learning with long input at test time; improves long-context understanding.
• Streaming + attention sinks: a fixed pool of anchor tokens + sliding KV window for long streams.
• External memory: LM-Infinite/InfLLM and related — cheaper than linear prompt growth.

C. Multilinguality

• Language alignment (instruction ≈ documents) or normalise to a single language.
• Per-language evaluation — gaps grow with sequence length; use multilingual benchmarks.

A cohesive production pipeline (sketch)

1. Retrieval: BM25 + bi-encoder (dense); take top-50.
2. Reranking: cross-encoder/LLM-ranker → top-k (e.g., 8–12).
3. Compression: LongLLMLingua-2/LongLLMLingua targeting ~3–5k tokens.
4. Ordering: extreme ordering — most important at the beginning and end; the middle = support.
5. Gating: quick NO-RESPONSE pass over passages.
6. Generation: self-consistency (n=3–7) + critic/reflection over citations.
7. 64k+ streams: enable streaming attention with sink tokens and a KV budget.

# Pseudocode
docs = retriever(q, k=50)
scored = cross_encoder.rank(q, docs)
sel = topk(scored, 10)
compressed = longllmlingua(sel, target_tokens=4000)
ordered = extreme_ordering(compressed)   # front + tail boost
kept = passage_gating(ordered, rule="NO-RESPONSE")
answer = llm.generate(kept, self_consistency=5, cite_sources=True)

How to measure progress rigorously

Test suite

• RULER / ONERULER: lengths 8k→128k, multiple needles, language variants (retrieval, tracking, aggregation).
• “Needle-in-the-Haystack” on steroids: multiple needles + “hard negatives.”
• Real tasks: QA/analyses with faithfulness scoring (does it cite the correct sources?).

Metrics

• Exact/EM, F1, citation precision/recall, latency, tokens/query cost, % of “abstain” answers.
• Curves for quality vs. length (8k, 16k, 32k, 64k…) and quality vs. context budget.

Simple A/B plan

1. Dataset of ~200–500 questions (min. 3 domains, 2 languages).
2. Conditions: baseline, +reranking, +compression, +ordering, +gating, +self-consistency.
3. Error analysis: separately “middle of context,” “distractors,” “overfill.”
4. Report: table with cost and quality gains per condition.

Implementation checklists

Lost in the Middle

• Cross-encoder reranking before inserting into the prompt.
• Extreme ordering (front+tail).
• Hierarchical summaries (map→reduce).
• If possible: LongRoPE/YaRN/PI + Ms-PoE; positional augmentation during SFT.

Distracting prompts

• Instruction “use only necessary information” + format with [EVIDENCE]→[ANS.].
• Few-shot with noise and filtering.
• Gating/abstention (NO-RESPONSE) at passage level.
• Self-consistency (n≥3) for sensitive tasks.

Very large contexts

• Compression (LongLLMLingua/LLMLingua-2) before feeding the LLM.
• Streaming attention + sink tokens for streams.
• External memory instead of “everything in the prompt.”
• ProLong/LIFT when you control model/inference.

Common anti-patterns (and how to fix them)

• “I’ll add more context; it will be better” → No. First select/compress, then order.
• “We’ll add 20 few-shot examples” → A shorter, well-curated pattern + format is often more robust.
• “Since the window is 128k, we’ll use 120k” → Practically effective is often 20–40% of the window. Keep the rest in an index/memory (heuristic → verify via A/B).

To wrap up

Long context is tempting, but not free. The best results come from selection and compression, conscious handling of positional bias (front+tail), and a pipeline that knows when to be silent (gating/abstention) and when to pull more sources (iterative retrieval). Architectural and training changes can add extra points, but first squeeze the most out of the application layer.

References and selected sources

1. Liu, N. F., et al. (2023/2024). Lost in the Middle: How Language Models Use Long Contexts / TACL version: PDF.
2. Shi, F., et al. (2023). Large Language Models Can Be Easily Distracted by Irrelevant Context (ICLR). Alt: publisher version.
3. Hsieh, C.-P., et al. (2024). RULER: What’s the Real Context Size of Your Long-Context Language Models? (+ repo).
4. Kim, Y., et al. (2025). OneRULER: Benchmarking multilingual long-context language models (OpenReview: page).
5. Hengle, A., et al. (2025). Can LLMs reason over extended multilingual contexts?
6. Ding, Y., et al. (2024). LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens.
7. Peng, B., et al. (2023). YaRN: Efficient Context Window Extension of Large Language Models (OpenReview: page).
8. Chen, S., et al. (2023). Position Interpolation (PI).
9. Gao, T., Wettig, A., Yen, H., Chen, D. (2024/2025). How to Train Long-Context Language Models (Effectively) (ACL Findings 2025: PDF; ProLong: repo).
10. Mao, Y., et al. (2024/2025). LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning.
11. Xiao, G., et al. (2023/ICLR-2024). Efficient Streaming Language Models with Attention Sinks (StreamingLLM) (GitHub: repo).
12. Han, C., et al. (2024). LM-Infinite: Zero-Shot Extreme Length Generalization for LLMs.
13. Xiao, C., et al. (2024). InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory.
14. Jiang, H., et al. (2023). LLMLingua; Jiang, H., et al. (2024). LongLLMLingua (ACL-Long 2024: PDF); Pan, Z., et al. (2024). LLMLingua-2 (MSR: page).
15. Asai, A., et al. (2023). Self-RAG: Learning to Retrieve, Generate, and Critique (site: selfrag.github.io).
16. Jiang, Z., et al. (2023). FLARE: Active Retrieval Augmented Generation (EMNLP 2023: page).
17. Zhang, Z., et al. (2024). Found in the Middle: Plug-and-Play Positional Encoding (Ms-PoE).
18. Amiraz, C., Cuconasu, F., Filice, S., Karnin, Z. (2025). The Distracting Effect: Understanding Irrelevant Passages in RAG (ACL 2025: PDF).
19. (Reranking) Nogueira, R., Cho, K. (2019). Passage Re-ranking with BERT.
20. (Dense late interaction) Santhanam, K., et al. (2022). ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (NAACL 2022: PDF).