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Found 14 papers, 11 papers with code
Self-Explore to Avoid the Pit: Improving the Reasoning Capabilities of Language Models with Fine-grained Rewards
Training on large amounts of rationales (i. e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs).
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.
KMMLU: Measuring Massive Multitask Language Understanding in Korean
We propose KMMLU, a new Korean benchmark with 35, 030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM.
Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?
Large language models (LLMs) are typically prompted to follow a single instruction per inference call.
LangBridge: Multilingual Reasoning Without Multilingual Supervision
We introduce LangBridge, a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision.
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging.
Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging
In this work, we study Reinforcement Learning from Personalized Human Feedback (RLPHF) problem, wherein LLMs are aligned to multiple (sometimes conflicting) preferences by modeling alignment as a Multi-Objective Reinforcement Learning (MORL) problem.
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
We first construct the Feedback Collection, a new dataset that consists of 1K fine-grained score rubrics, 20K instructions, and 100K responses and language feedback generated by GPT-4.
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction.
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2. 24% (Flan-T5 3B) and +2. 37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until the max length by a +13. 98% margin.
Ranked #1 on
on BIG-bench (SNARKS)
Common Sense Reasoning
Common Sense Reasoning (Zero-Shot)
+7
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
To improve the correctness of the explanations, fine-tuning language models with explanation data is needed.
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
Ranked #9 on
Question Answering
on StoryCloze
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.
Ranked #2 on
Text Summarization
on DialogSum
Can Language Models perform Abductive Commonsense Reasoning?
Abductive Reasoning is a task of inferring the most plausible hypothesis given a set of observations.
