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Found 17 papers, 2 papers with code
Prompting LLMs to Compose Meta-Review Drafts from Peer-Review Narratives of Scholarly Manuscripts
One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves understanding the core contributions, strengths, and weaknesses of a scholarly manuscript based on peer-review narratives from multiple experts and then summarizing those multiple experts' perspectives into a concise holistic overview.
Word Embeddings Revisited: Do LLMs Offer Something New?
Learning meaningful word embeddings is key to training a robust language model.
SNNLP: Energy-Efficient Natural Language Processing Using Spiking Neural Networks
As spiking neural networks receive more attention, we look toward applications of this computing paradigm in fields other than computer vision and signal processing.
FaNS: a Facet-based Narrative Similarity Metric
Similar Narrative Retrieval is a crucial task since narratives are essential for explaining and understanding events, and multiple related narratives often help to create a holistic view of the event of interest.
Redundancy Aware Multi-Reference Based Gainwise Evaluation of Extractive Summarization
While very popular for evaluating extractive summarization task, the ROUGE metric has long been criticized for its lack of semantic awareness and its ignorance about the ranking quality of the summarizer.
ChatGPT as your Personal Data Scientist
Instead, envision an intelligent agent capable of assisting users in conducting AutoML tasks through intuitive, natural conversations without requiring in-depth knowledge of the underlying machine learning (ML) processes.
TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks
However, conducting such benchmarking studies is challenging because of the large variations in LLMs' performance when different prompt types/styles are used and different degrees of detail are provided in the prompts.
Processing Natural Language on Embedded Devices: How Well Do Transformer Models Perform?
This paper presents a performance study of transformer language models under different hardware configurations and accuracy requirements and derives empirical observations about these resource/accuracy trade-offs.
Zero-Shot Multi-Label Topic Inference with Sentence Encoders
Sentence encoders have indeed been shown to achieve superior performances for many downstream text-mining tasks and, thus, claimed to be fairly general.
Joint Upper & Lower Bound Normalization for IR Evaluation
Experiments on two different data-sets with eight Learning-to-Rank (LETOR) methods demonstrate the following properties of the new LB normalized metric: 1) Statistically significant differences (between two methods) in terms of original metric no longer remain statistically significant in terms of Upper Lower (UL) Bound normalized version and vice-versa, especially for uninformative query-sets.
Multi-Narrative Semantic Overlap Task: Evaluation and Benchmark
In this paper, we introduce an important yet relatively unexplored NLP task called Multi-Narrative Semantic Overlap (MNSO), which entails generating a Semantic Overlap of multiple alternate narratives.
AutoML to Date and Beyond: Challenges and Opportunities
AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research.
TILM: Neural Language Models with Evolving Topical Influence
Specifically, we propose a novel language model called Topical Influence Language Model (TILM), which is a novel extension of a neural language model to capture the influences on the contents in one text stream by the evolving topics in another related (or possibly same) text stream.
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data
In this paper, we tackle the challenge of defining useful prediction problems on event-driven time-series data.
JIM: Joint Influence Modeling for Collective Search Behavior
This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would pose a joint influence on the user search behavior rather than posing influence independently.
On Application of Learning to Rank for E-Commerce Search
In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-to-cart ratios, order rates, and revenue.
