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Found 10 papers, 5 papers with code
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences and mitigate issues like toxicity and hallucinations.
GEO: Generative Engine Optimization
We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries.
QualEval: Qualitative Evaluation for Model Improvement
In this work, we address the shortcomings of quantitative metrics by proposing QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement.
Anthropomorphization of AI: Opportunities and Risks
With widespread adoption of AI systems, and the push from stakeholders to make it human-like through alignment techniques, human voice, and pictorial avatars, the tendency for users to anthropomorphize it increases significantly.
C-STS: Conditional Semantic Textual Similarity
Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of similarity between a pair of sentences, and has broad application in fields such as information retrieval and natural language understanding.
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models.
Hyperbolic Image-Text Representations
Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs.
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service.
Lila: A Unified Benchmark for Mathematical Reasoning
Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling.
Ranked #1 on
Mathematical Reasoning
on Lila (OOD)
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data.
