Search Results for author:
Found 10 papers, 6 papers with code
On the Role of Summary Content Units in Text Summarization Evaluation
At the heart of the Pyramid evaluation method for text summarization lie human written summary content units (SCUs).
RewardBench: Evaluating Reward Models for Language Modeling
In this paper, we present RewardBench, a benchmark dataset and code-base for evaluation, to enhance scientific understanding of reward models.
L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects
In this paper, we propose a language agent with chain-of-3D-thoughts (L3GO), an inference-time approach that can reason about part-based 3D mesh generation of unconventional objects that current data-driven diffusion models struggle with.
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported.
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation
We present NovaCOMET, an open commonsense knowledge model, that combines the best aspects of knowledge and general task models.
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
We analyze the effect of alignment tuning by examining the token distribution shift between base LLMs and their aligned counterpart.
Agent Lumos: Unified and Modular Training for Open-Source Language Agents
To foster generalizable agent learning, we collect large-scale, unified, and high-quality training annotations derived from diverse ground-truth reasoning rationales across various complex interactive tasks.
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure prompting can often be limited.
Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization
To prevent the costly and inefficient use of resources on low-quality annotations, we want a method for creating a pool of dependable annotators who can effectively complete difficult tasks, such as evaluating automatic summarization.
