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Found 18 papers, 14 papers with code
What Are Tools Anyway? A Survey from the Language Model Perspective
Language models (LMs) are powerful yet mostly for text generation tasks.
RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems
RAGGED offers further insights into LMs' context utilization habits, where we find that encoder-decoder models rely more on contexts and are thus more sensitive to retrieval quality, while decoder-only models tend to rely on knowledge memorized during training.
TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks
Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs.
Learning to Filter Context for Retrieval-Augmented Generation
To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time.
API-Assisted Code Generation for Question Answering on Varied Table Structures
A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms.
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information.
SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #43 on
Code Generation
on MBPP
Execution-Based Evaluation for Open-Domain Code Generation
To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset.
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers.
Ranked #1 on
Passage Retrieval
on Natural Questions
DocPrompting: Generating Code by Retrieving the Docs
Publicly available source-code libraries are continuously growing and changing.
Table Retrieval May Not Necessitate Table-specific Model Design
In this work, we focus on the task of table retrieval, and ask: "is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result?"
MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages
While there has been a recent burgeoning of applications at the intersection of natural and programming languages, such as code generation and code summarization, these applications are usually English-centric.
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
HiTab provides 10, 686 QA pairs and descriptive sentences with well-annotated quantity and entity alignment on 3, 597 tables with broad coverage of table hierarchies and numerical reasoning types.
Intrinsic Knowledge Evaluation on Chinese Language Models
Recent NLP tasks have benefited a lot from pre-trained language models (LM) since they are able to encode knowledge of various aspects.
TUTA: Tree-based Transformers for Generally Structured Table Pre-training
First, we devise a unified tree-based structure, called a bi-dimensional coordinate tree, to describe both the spatial and hierarchical information of generally structured tables.
FastBERT: a Self-distilling BERT with Adaptive Inference Time
Pre-trained language models like BERT have proven to be highly performant.
K-BERT: Enabling Language Representation with Knowledge Graph
For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge.
