Search Results for author:
Found 13 papers, 9 papers with code
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code
Training several popular base models with this corpus significantly improves their mathematical abilities, leading to the creation of the MathCoder2 family of models.
Empowering Character-level Text Infilling by Eliminating Sub-Tokens
Alternately, some approaches considered character-level infilling, but they relied on predicting sub-tokens in inference, yet this strategy diminished ability in character-level infilling tasks due to the large perplexity of the model on sub-tokens.
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation
Inspired by this, we present ReflectionCoder, a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance.
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
In this paper, we present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities.
Ranked #6 on
Math Word Problem Solving
on SVAMP
(using extra training data)
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel re-training strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks.
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval
However, we find that the performance of the cross-encoder re-ranker is heavily influenced by the number of training samples and the quality of negative samples, which is hard to obtain in the cross-lingual setting.
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
In this paper, we propose to mine and generate self-supervised training data based on a large-scale unlabeled corpus.
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation.
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages.
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
On the multilingual sentence retrieval task Tatoeba, our model achieves new SOTA results among methods without using bilingual data.
GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding
Forms are a common type of document in real life and carry rich information through textual contents and the organizational structure.
