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Found 6 papers, 6 papers with code
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
In this work, we introduce $\textit{weak-to-strong search}$, framing the alignment of a large language model as a test-time greedy search to maximize the log-likelihood difference between small tuned and untuned models while sampling from the frozen large model.
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) 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.
