Foundational challenges in assuring alignment and safety of large language models
This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …
language models (LLMs). These challenges are organized into three different categories …
LLM4SR: A Survey on Large Language Models for Scientific Research
In recent years, the rapid advancement of Large Language Models (LLMs) has transformed
the landscape of scientific research, offering unprecedented support across various stages …
the landscape of scientific research, offering unprecedented support across various stages …
Code repair with llms gives an exploration-exploitation tradeoff
Iteratively improving and repairing source code with large language models (LLMs), known
as refinement, has emerged as a popular way of generating programs that would be too …
as refinement, has emerged as a popular way of generating programs that would be too …
Reasoning abilities of large language models: In-depth analysis on the abstraction and reasoning corpus
The existing methods for evaluating the inference abilities of Large Language Models
(LLMs) have been predominantly results-centric, making it challenging to assess the …
(LLMs) have been predominantly results-centric, making it challenging to assess the …
Explaining Datasets in Words: Statistical Models with Natural Language Parameters
To make sense of massive data, we often first fit simplified models and then interpret the
parameters; for example, we cluster the text embeddings and then interpret the mean …
parameters; for example, we cluster the text embeddings and then interpret the mean …
Automated statistical model discovery with language models
Statistical model discovery involves a challenging search over a vast space of models
subject to domain-specific modeling constraints. Efficiently searching over this space …
subject to domain-specific modeling constraints. Efficiently searching over this space …
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
Chaining language model (LM) calls as composable modules is fueling a new powerful way
of programming. However, ensuring that LMs adhere to important constraints remains a key …
of programming. However, ensuring that LMs adhere to important constraints remains a key …
Literature meets data: A synergistic approach to hypothesis generation
AI holds promise for transforming scientific processes, including hypothesis generation. Prior
work on hypothesis generation can be broadly categorized into theory-driven and data …
work on hypothesis generation can be broadly categorized into theory-driven and data …
Neural networks for abstraction and reasoning
For half a century, artificial intelligence research has attempted to reproduce the human
qualities of abstraction and reasoning-creating computer systems that can learn new …
qualities of abstraction and reasoning-creating computer systems that can learn new …
Doing experiments and revising rules with natural language and probabilistic reasoning
We give a model of how to infer natural language rules by doing experiments. The model
integrates Large Language Models (LLMs) with Monte Carlo algorithms for probabilistic …
integrates Large Language Models (LLMs) with Monte Carlo algorithms for probabilistic …