AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
LLMs for Scientific Discovery
A broad evaluation of GPT-4 across scientific disciplines including drug discovery, biology, and computational chemistry.
Contrastive Chain-of-Thought
Proposes contrastive CoT prompting where models see both valid *and* invalid reasoning demonstrations to reduce reasoning errors.
Survey on Language Models for Code
A comprehensive survey of LLMs for code covering 50+ models, 30+ evaluation tasks, and 500 related works.
Learning to Filter Context for RAG (FILCO)
CMU's FILCO improves RAG by training a dedicated model to filter retrieved contexts before they reach the generator.
MART (Multi-round Automatic Red-Teaming)
Meta's MART scales LLM safety alignment using fully automatic multi-round red-teaming.
Hallucination in LLMs Survey
A comprehensive survey of hallucination in LLMs, covering taxonomy, causes, evaluation, and mitigation.
In-Context Learning Generalization Limits
Investigates whether transformers' in-context learning can generalize beyond the distribution of their pretraining data.
Rephrase and Respond (RaR)
An effective prompting method where the LLM rephrases and expands the user's question before answering it.
On the Road with GPT-4V
An exhaustive evaluation of GPT-4V applied to autonomous driving scenarios.
FreshLLMs (FreshQA)
Introduces FreshQA, a dynamic benchmark designed to stress-test LLMs on time-sensitive knowledge.
Evaluating LLMs Survey
A comprehensive survey of LLM evaluation covering benchmarks, methodologies, and open problems.
Battle of the Backbones
A large-scale benchmarking framework that compares vision backbones across a diverse suite of computer vision tasks.
ChipNeMo (LLMs for Chip Design)
NVIDIA's ChipNeMo applies domain-adapted LLMs to industrial chip design workflows.
EmotionPrompt
Microsoft researchers show that appending emotional stimuli to prompts reliably improves LLM performance across 45 tasks.
Zephyr
Hugging Face's Zephyr-7B is a 7B parameter LLM whose chat performance rivals much larger chat models aligned with human feedback.
Fact-Checking with LLMs
Investigates the fact-checking capabilities of frontier LLMs across multiple languages and claim types.
LLMs Meet New Knowledge
A benchmark that evaluates how well LLMs handle new knowledge beyond their training cutoff.
Min-K% Prob (Detecting Pretraining Data)
Proposes Min-K% Prob as an effective detection method for determining whether specific text was in an LLM's pretraining data.
Branch-Solve-Merge (BSM)
BSM decomposes LLM tasks into parallel sub-tasks via three LLM-programmed modules: branch, solve, and merge.
Llemma
Llemma is an open LLM for mathematics built via continued pretraining of Code Llama on the Proof-Pile-2 dataset.
LLMs for Software Engineering
A comprehensive survey of LLMs for software engineering covering models, tasks, evaluation, and open challenges.
Self-RAG
Self-RAG trains an LM to adaptively retrieve, generate, and self-critique using special reflection tokens.
RAG for Long-Form QA
Explores retrieval-augmented LMs specifically on long-form question answering, where RAG failures are more subtle.
GenBench
A Nature Machine Intelligence paper framework for characterizing and understanding generalization research in NLP.