Excited to share our paper "How Long Reasoning Chains Influence LLMs’ Judgment of Answer Factuality" \w @bikeping got accepted to ACL 2026, with an Oral recommendation from the Senior Area Chair! Paper: arxiv.org/pdf/2604.06756 Code: github.com/Trustworthy-Infor… 🎉 Here's what we did 🧵 💡Motivation LLM-as-a-Judge often fails because judges don't know the correct answer — and have no extra information to reference. Can the reasoning trace serves as additional evidence that help the judges to judge more accurately? 📖Concrete example: Q: Who was the first Nobel Physics laureate? A: Einstein The judge doesn't know if that's right. But the reasoning says "Einstein won his first Nobel in 1921" — while the first prize was awarded in 1901. Caught! 🙅 Sounds great… but is it really that simple? 🔦 What we did? TL;DR: Reasoning traces are a double-edged sword. LLM judges still can't consistently distinguish "actually correct" from "sounds correct." We studied this across 4 datasets × 10+ judge models (GPT-4o, Claude Sonnet 4.5, DeepSeek-v3.1…). Two key findings: ❌ Weak judges are almost completely fooled. In NQ, only 23.2% of answers are correct — but weak models accept up to 88% when reasoning looks fluent. They judge style, not substance. ✅ Strong judges are smarter, but not perfect. DeepSeek-v3.1's alignment improves from 63.4% → 76.2% on NQ. But even strong judges get misled by high-quality reasoning chains. Just like humans: non-experts get sweet-talked, experts push back 😄 Controlled experiments on reasoning chain features: 1. Fluency is the first gate: break the reasoning flow, and most models mark it “incorrect,” even if it’s right. 2. Factuality is important: counterfactuals reduce pass rates, but adding more errors doesn’t increase sensitivity—the evaluator isn’t counting them. 3. Position matters: errors at the start hurt most; errors at the end matter less.

Our paper "Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception" will be presented on July 30th from 11:00 to 12:30 at Hall 5X, #195. Welcome to drop by and have a discussion! #ACL2025NLP

🥳Happy to share that our paper "Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception" has been accepted by #ACL2025! We explore leveraging LLMs' internal states to improve their knowledge boundary perception from efficiency and risk perspectives.