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Found 32 papers, 22 papers with code
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning
The capacity for complex mathematical reasoning is a key benchmark for artificial intelligence.
Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique
Traditional inference time scaling methods utilize scalar reward signals from process reward models to evaluate candidate reasoning steps, but these scalar rewards lack the nuanced qualitative information essential for understanding and justifying each step.
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
By training exclusively on the initial prefix substrings (as few as 8 tokens), UPFT removes the need for labeled data or exhaustive sampling.
Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
To address underthinking, we propose a decoding strategy with thought switching penalty TIP that discourages premature transitions between thoughts, encouraging deeper exploration of each reasoning path.
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
The remarkable performance of models like the OpenAI o1 can be attributed to their ability to emulate human-like long-time thinking during inference.
Draft Model Knows When to Stop: A Self-Verification Length Policy for Speculative Decoding
Speculative Decoding (SD) has become an important technique in accelerating the inference speed of large language models.
Weak-to-Strong Preference Optimization: Stealing Reward from Weak Aligned Model
Aligning language models (LMs) with human preferences has become a key area of research, enabling these models to meet diverse user needs better.
VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking
Current LiDAR point cloud-based 3D single object tracking (SOT) methods typically rely on point-based representation network.
P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds
Moreover, under the same point-based representation, P2P-point outperforms the previous motion tracker M$^2$Track by \textbf{3. 3\%} and \textbf{6. 7\%} on the KITTI and NuScenes, while running at a considerably high speed of \textbf{107 Fps} on a single RTX3090 GPU.
Evaluating Knowledge-based Cross-lingual Inconsistency in Large Language Models
This paper investigates the cross-lingual inconsistencies observed in Large Language Models (LLMs), such as ChatGPT, Llama, and Baichuan, which have shown exceptional performance in various Natural Language Processing (NLP) tasks.
MarkLLM: An Open-Source Toolkit for LLM Watermarking
However, the abundance of LLM watermarking algorithms, their intricate mechanisms, and the complex evaluation procedures and perspectives pose challenges for researchers and the community to easily experiment with, understand, and assess the latest advancements.
Improving Open-Ended Text Generation via Adaptive Decoding
Current language models decode text token by token according to probabilistic distribution, and determining the appropriate candidates for the next token is crucial to ensure generation quality.
Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method
Bargaining is an important and unique part of negotiation between humans.
Is Self-knowledge and Action Consistent or Not: Investigating Large Language Model's Personality
In this study, we delve into the validity of conventional personality questionnaires in capturing the human-like personality traits of Large Language Models (LLMs).
Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models
Furthermore, we analyze two key factors that contribute to the cross-lingual consistency in text watermarking and propose X-SIR as a defense method against CWRA.
Unsupervised Sign Language Translation and Generation
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns from abundant single-modality (text and video) data without parallel sign language data.
Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model
In this work, we investigate the potential of employing the QE model as the reward model to predict human preferences for feedback training.
Towards Category Unification of 3D Single Object Tracking on Point Clouds
Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner.
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents
We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging and identifying safety risks given agent interaction records.
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models
The best candidate is finally selected from this set based on the BLEURT score.
Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models
Ideally, an advanced agent should possess the ability to accurately describe a given word using an aggressive description while concurrently maximizing confusion in the conservative description, enhancing its participation in the game.
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution.
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation
Tense inconsistency frequently occurs in machine translation.
Exploring Human-Like Translation Strategy with Large Language Models
Compared to typical machine translation that focuses solely on source-to-target mapping, LLM-based translation can potentially mimic the human translation process which might take preparatory steps to ensure high-quality translation.
OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking
By integrating the derived classification scores with the center-ness scores, the resulting network can effectively suppress interference proposals and further mitigate task misalignment.
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback
Therefore, we propose ParroT, a framework to enhance and regulate the translation abilities during chat based on open-source LLMs (e. g., LLaMA), human-written translation and feedback data.
GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss
Incorporating this transformer-based voting scheme into 3D RPN, a novel Siamese method dubbed GLT-T is developed for 3D single object tracking on point clouds.
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds
Technically, a global-local transformer (GLT) module is employed to integrate object- and patch-aware prior into seed point features to effectively form strong feature representation for geometric positions of the seed points, thus providing more robust and accurate cues for offset learning.
Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Task
Finally, our unconstrained system achieves BLEU scores of 17. 0 and 30. 4 for English to/from Livonian.
Learning Localization-aware Target Confidence for Siamese Visual Tracking
In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA.
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation
By carefully designing experiments, we identify two representative characteristics of the data gap in source: (1) style gap (i. e., translated vs. natural text style) that leads to poor generalization capability; (2) content gap that induces the model to produce hallucination content biased towards the target language.
