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Found 5 papers, 4 papers with code
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
By conducting a detailed analysis of real multi-turn dialogue data, we construct a three-tier hierarchical ability taxonomy comprising 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks.
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs).
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!
Large language models (LLMs) need to undergo safety alignment to ensure safe conversations with humans.
Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey
Large Language Models (LLMs) are now commonplace in conversation applications.
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization
A single language model (LM), despite aligning well with an average labeler through reinforcement learning from human feedback (RLHF), may not universally suit diverse human preferences.
