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Found 19 papers, 10 papers with code
A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
In this survey, we categorize existing methods along two orthogonal dimensions: (1) Regimes, which define the stage at which reasoning is achieved (either at inference time or through dedicated training); and (2) Architectures, which determine the components involved in the reasoning process, distinguishing between standalone LLMs and agentic compound systems that incorporate external tools, and multi-agent collaborations.
StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs
The rapid development of large language models (LLMs) necessitates robust, unbiased, and scalable methods for evaluating their capabilities.
The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense
The problem causes these defense methods to exhibit unintended abstention, even in the presence of benign inputs, thereby undermining their reliability in faithfully defending against attacks.
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks
We validate CR-Planner on challenging domain-knowledge-intensive and reasoning-heavy tasks, including competitive programming, theorem-driven math reasoning, and complex domain retrieval problems.
Describe-then-Reason: Improving Multimodal Mathematical Reasoning through Visual Comprehension Training
Open-source multimodal large language models (MLLMs) excel in various tasks involving textual and visual inputs but still struggle with complex multimodal mathematical reasoning, lagging behind proprietary models like GPT-4V(ision) and Gemini-Pro.
Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?
One key finding in psychology is that compared with irrelevant past experiences, recalling relevant ones can help humans better handle new tasks.
How Much are Large Language Models Contaminated? A Comprehensive Survey and the LLMSanitize Library
With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming critical.
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
Large Language Models (LLMs) have demonstrated significant potential in handling complex reasoning tasks through step-by-step rationale generation.
Improving In-context Learning via Bidirectional Alignment
Large language models (LLMs) have shown impressive few-shot generalization on many tasks via in-context learning (ICL).
ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?
Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce.
UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models
Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system.
SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning
We present SeaEval, a benchmark for multilingual foundation models.
Exploring Self-supervised Logic-enhanced Training for Large Language Models
Existing efforts to improve logical reasoning ability of language models have predominantly relied on supervised fine-tuning, hindering generalization to new domains and/or tasks.
Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models
This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance.
Retrieving Multimodal Information for Augmented Generation: A Survey
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world.
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning
Logical reasoning is of vital importance to natural language understanding.
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REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
Pre-trained Language Models (PLMs) have achieved great success on Machine Reading Comprehension (MRC) over the past few years.
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction
Neural models have achieved great success on machine reading comprehension (MRC), many of which typically consist of two components: an evidence extractor and an answer predictor.
