Challenge: Existing studies on verbal leakage cues do not address their impact on models' validity.
Approach: They propose to use LIWC to show verbal leakage cues in lie detection datasets to understand their effect on data collection and examine their validity.
Outcome: The proposed models with more strong verbal leakage cue categories perform better than models trained on a dataset with only a greater number of strong cues.

Similar Papers

Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies (2020.tacl-1)

Copied to clipboard

Challenge: LieCatcher collects ratings of perceived deception using corpus of deceptive and truthful interviews . acoustic-prosodic and linguistic characteristics of language trusted and mistrusted are not reliable cues .
Approach: They used a game framework to collect ratings of perceived deception using deceptive and truthful interviews to understand how perception aligns with reality.
Outcome: The proposed framework detects deception using a corpus of deceptive and truthful interviews.
Detecting Concealed Information in Text and Speech (P19-1)

Copied to clipboard

Challenge: despite the importance and potential impact of detecting concealed information, research on detecting it has been scarce.
Approach: They propose a multi-task learning framework that automatically detects concealed information from text and speech using acoustic-prosodic, linguistic, and individual feature sets.
Outcome: The proposed framework outperforms human performance by 15% in acoustic, linguistic, and individual features.
Linguistic Cues to Deception and Perceived Deception in Interview Dialogues (N18-1)

Copied to clipboard

Challenge: a recent study examined deception detection in several domains, including fake reviews, mock crime scenes, and opinions about topics such as abortion or the death penalty.
Approach: They analyze linguistic features in truthful and deceptive interview dialogues . they also examine interviewer perceptions of deception, identifying characteristics of deceptives .
Outcome: The proposed model outperforms human classifications using linguistic features and individual traits.
Investigating How Pre-training Data Leakage Affects Models’ Reproduction and Detection Capabilities (2025.emnlp-main)

Copied to clipboard

Challenge: Existing studies do not examine how leaked instances in training datasets influence LLMs’ output and detection capabilities.
Approach: They conduct an experimental survey to examine the relationship between data leakage in training datasets and its effects on the generation and detection by Large Language Models (LLMs).
Outcome: The results show that enhancing leakage detection through few-shot learning can help mitigate the impact of the leakage rate in the training data on detection performance.
“Does it Matter When I Think You Are Lying?” Improving Deception Detection by Integrating Interlocutor’s Judgements in Conversations (2021.findings-acl)

Copied to clipboard

Challenge: Existing methods for deception detection are based on interrogator's perceptions of truth-bias . despite its frequent occurrences, human is not good at detecting deceptions despite inclination of truth bias .
Approach: They propose a Judgmental-Enhanced Automatic Deception Detection Network that explicitly considers interrogator's perceived truths-deceptions with three types of speechlanguage features extracted during a conversation.
Outcome: The proposed method outperforms the current state-of-the-art approach without conditioning on interrogator's judgements.
BERTective: Language Models and Contextual Information for Deception Detection (2021.eacl-main)

Copied to clipboard

Challenge: Existing methods to classify texts as truthful or deceptive are limited by the context of the text being analyzed.
Approach: They propose to use a corpus of Italian dialogues to classify texts as truthful or deceptive.
Outcome: The proposed models show that not all contexts are equally useful to the task.
To Tell The Truth: Language of Deception and Language Models (2024.naacl-long)

Copied to clipboard

Challenge: Existing evidence of people’s ability to discern truth from text-based false information is scarce.
Approach: They propose to use a large language model to learn discernible cues from TV game show data to investigate whether textual cue is more likely to detect fraud .
Outcome: The proposed model detects novel but accurate language cues in many cases where humans failed to detect deception.
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)

Copied to clipboard

Challenge: Recent studies show that pre-trained masked language models can be factual knowledge bases.
Approach: They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a .
Outcome: The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases .
Hidden in Plain Sight: Evaluation of the Deception Detection Capabilities of LLMs in Multimodal Settings (2025.acl-long)

Copied to clipboard

Challenge: Detecting deception in an increasingly digital world is a critical and challenging task.
Approach: They evaluate the performance of both open-source and proprietary LLMs on three datasets . they find that fine-tuned LLM achieve state-of-the-art performance on textual deception detection .
Outcome: The proposed models achieve state-of-the-art on textual deception detection, whereas LMMs struggle to fully leverage multimodal cues.
Revisiting the Effects of Leakage on Dependency Parsing (2022.findings-acl)

Copied to clipboard

Challenge: Recent work shows that treebank size and linguistic variation are important factors that explain the variation in dependency parsing performance.
Approach: They propose a measure of leakage that explains and correlates with observed performance variation.
Outcome: The proposed measure explains and correlates with observed performance variation.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations