Search Results for author:
Found 15 papers, 13 papers with code
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
Training large language models to follow instructions makes them perform better on a wide range of tasks, generally becoming more helpful.
Do Language Models Know When They're Hallucinating References?
While other types of LM hallucinations are also important, we propose hallucinated references as the "drosophila" of research on hallucination in large language models (LLMs), as they are particularly easy to study.
string2string: A Modern Python Library for String-to-String Algorithms
It includes traditional algorithmic solutions as well as recent advanced neural approaches to tackle various problems in string alignment, distance measurement, lexical and semantic search, and similarity analysis -- along with several helpful visualization tools and metrics to facilitate the interpretation and analysis of these methods.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding
In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality.
Scaling Instruction-Finetuned Language Models
We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).
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Cross-Lingual Question Answering
Multi-task Language Understanding
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Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models.
Language Models are Multilingual Chain-of-Thought Reasoners
Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment.
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models
We propose a method for arbitrary textual style transfer (TST)--the task of transforming a text into any given style--utilizing general-purpose pre-trained language models.
Monte Carlo Tree Search for Interpreting Stress in Natural Language
Using a dataset of Reddit posts that exhibit stress, we demonstrate the ability of our MCTS algorithm to identify interpretable explanations for a person's feeling of stress in both a context-dependent and context-independent manner.
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
We introduce three memory-augmented Recurrent Neural Networks (MARNNs) and explore their capabilities on a series of simple language modeling tasks whose solutions require stack-based mechanisms.
LSTM Networks Can Perform Dynamic Counting
In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations.
On Evaluating the Generalization of LSTM Models in Formal Languages
Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a dominant model for language processing.
