8,858 research outputs found
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency
Semantic consistency of a language model is broadly defined as the model's
ability to produce semantically-equivalent outputs, given
semantically-equivalent inputs. We address the task of assessing
question-answering (QA) semantic consistency of contemporary large language
models (LLMs) by manually creating a benchmark dataset with high-quality
paraphrases for factual questions, and release the dataset to the community.
We further combine the semantic consistency metric with additional
measurements suggested in prior work as correlating with LLM QA accuracy, for
building and evaluating a framework for factual QA reference-less performance
prediction -- predicting the likelihood of a language model to accurately
answer a question. Evaluating the framework on five contemporary LLMs, we
demonstrate encouraging, significantly outperforming baselines, results.Comment: EMNLP2023 GEM workshop, 17 page
ANTIQUE: A Non-Factoid Question Answering Benchmark
Considering the widespread use of mobile and voice search, answer passage
retrieval for non-factoid questions plays a critical role in modern information
retrieval systems. Despite the importance of the task, the community still
feels the significant lack of large-scale non-factoid question answering
collections with real questions and comprehensive relevance judgments. In this
paper, we develop and release a collection of 2,626 open-domain non-factoid
questions from a diverse set of categories. The dataset, called ANTIQUE,
contains 34,011 manual relevance annotations. The questions were asked by real
users in a community question answering service, i.e., Yahoo! Answers.
Relevance judgments for all the answers to each question were collected through
crowdsourcing. To facilitate further research, we also include a brief analysis
of the data as well as baseline results on both classical and recently
developed neural IR models
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
In this paper, the answer selection problem in community question answering
(CQA) is regarded as an answer sequence labeling task, and a novel approach is
proposed based on the recurrent architecture for this problem. Our approach
applies convolution neural networks (CNNs) to learning the joint representation
of question-answer pair firstly, and then uses the joint representation as
input of the long short-term memory (LSTM) to learn the answer sequence of a
question for labeling the matching quality of each answer. Experiments
conducted on the SemEval 2015 CQA dataset shows the effectiveness of our
approach.Comment: 6 page
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
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