mlsanityData to 7 Oct 2026

One Battle After Another: Probing LLMs' Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework

arXiv 2511.03508 ↗First posted 5 Nov 2025cs.CLcited by 9

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Abstract (arXiv)

Evaluating LLMs' instruction-following ability in multi-topic dialogues is essential yet challenging. Existing benchmarks are limited to a fixed number of turns, susceptible to saturation and failing to account for users' interactive experience. In this work, we propose a novel framework featuring a three-layer tracking mechanism and a query synthesis agent to mimic sequential user behaviors. Grounded in Flow Theory, we introduce process-centric metrics and terminate a conversational evaluation only upon exhausting user patience. Leveraging this framework, we present EvolIF, an evolving benchmark covering 12 constraint groups. Our analysis reveals deficiencies in failure recovery and fine-grained instruction following, with performance stratification becoming evident as conversational depth increases. GPT-5 demonstrates the most sustained resilience, maintaining a 66.40% robustness score, outperforming Gemini-3-Pro by 5.59%, while other models lag behind. Data and code will be released at https://github.com/JiaQiSJTU/EvolIF.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 5 Nov 2025.

Builds on

22 of 42 references.

References · 22

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2024From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipelinecited by 484
  3. 2024Self-Preference Bias in LLM-as-a-Judgecited by 248
  4. 2024MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialoguescited by 247
  5. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 236
  6. 2025MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMscited by 171
  7. 2024Benchmarking Complex Instruction-Following with Multiple Constraints Compositioncited by 147
  8. 2023Can Large Language Models Understand Real-World Complex Instructions?cited by 69
  9. 2024CFBench: A Comprehensive Constraints-Following Benchmark for LLMscited by 65
  10. 2024Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussionscited by 40
  11. 2024FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMscited by 36
  12. 2025Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judgecited by 35
10 more
  1. 2025StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Followingcited by 28
  2. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 25
  3. 2024The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Modelscited by 18
  4. 2025MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluationcited by 15
  5. 2025Can Language Models Follow Multiple Turns of Entangled Instructions?cited by 14
  6. 2025CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generationcited by 14
  7. 2024SimulBench: Evaluating Language Models with Creative Simulation Taskscited by 7
  8. 2025From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactionscited by 7
  9. 2023Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendationcited by 3
  10. 2024DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential Recommendationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 9.

9 papers.

  1. 2026When Attention Closes: How LLMs Lose the Thread in Multi-Turn Interactioncited by 3
  2. 2026Where do LLMs Fall Short in CBT-Guided Affective Reasoning?cited by 1
  3. 2026Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judgingcited by 1
  4. 2026Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challengescited by 1
  5. 2026Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understandingcited by 0
  6. 2026Dead text or binding clause? Measuring and restoring constraint influence in black-box LLM dialoguescited by 0
  7. 2026The Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?cited by 0
  8. 2026When Users Change Their Minds: Measuring and Repairing Intent Drift in LLM Agentscited by 0
  9. 2026The Effects of Incremental Instruction Delivery on Language-Model Creative Writingcited by 0