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Found 25 papers, 13 papers with code
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
This work offers a unified, controllable framework for advancing logical reasoning in LLMs.
KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation
Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities.
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
We introduce Seed1. 5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks.
Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation
To address this issue, we introduce an order-centric data augmentation framework based on commutativity in logical reasoning.
Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following
Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs).
Application of machine learning algorithm in temperature field reconstruction
To address this, the study employs Proper Orthogonal Decomposition (POD) and sparse representation methods to reconstruct the temperature field based on temperature data from a limited number of local measurement points.
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models
It is crucial for large language models (LLMs) to follow instructions that involve multiple constraints.
QUILL: Quotation Generation Enhancement of Large Language Models
To improve the LLMs' quotation generation abilities, we construct a bilingual knowledge base that is broad in scope and rich in dimensions, containing up to 32, 022 quotes.
Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
To address these limitations, we propose Progressive Thought Refinement (PTR), a framework that enables LLMs to refine their responses progressively.
Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models
Specifically, we introduce COG, a two-stage framework with COncept-Guided vision-language models.
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models
It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i. e. Complex Instructions Following).
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
Definition bias is a negative phenomenon that can mislead models.
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
In our experiments, we find that atomic skills can not spontaneously generalize to compositional tasks.
Small Language Model Can Self-correct
In this paper, we introduce the \underline{I}ntrinsic \underline{S}elf-\underline{C}orrection (ISC) in generative language models, aiming to correct the initial output of LMs in a self-triggered manner, even for those small LMs with 6 billion parameters.
Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception
Hence, we present a framework to enhance the quantitative reasoning ability of language models based on dimension perception.
Can Large Language Models Understand Real-World Complex Instructions?
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability.
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation
Similes play an imperative role in creative writing such as story and dialogue generation.
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs).
Domain Mastery Benchmark: An Ever-Updating Benchmark for Evaluating Holistic Domain Knowledge of Large Language Model--A Preliminary Release
Domain knowledge refers to the in-depth understanding, expertise, and familiarity with a specific subject, industry, field, or area of special interest.
BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark
Our aim is to facilitate research in the development of NLP within the Chinese financial domain.
MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base
The ability to understand and generate similes is an imperative step to realize human-level AI.
Language Models as Knowledge Embeddings
In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods.
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Can Pre-trained Language Models Interpret Similes as Smart as Human?
In this paper, we investigate the ability of PLMs in simile interpretation by designing a novel task named Simile Property Probing, i. e., to let the PLMs infer the shared properties of similes.
