Are Large Language Models Ready for Travel Planning?

• URL: http://arxiv.org/abs/2410.17333v1
• Date: Tue, 22 Oct 2024 18:08:25 GMT
• Title: Are Large Language Models Ready for Travel Planning?
• Authors: Ruiping Ren, Xing Yao, Shu Cole, Haining Wang,
• Abstract summary: Large language models (LLMs) show promise in hospitality and tourism, their ability to provide unbiased service across demographic groups remains unclear. This paper explores gender and ethnic biases when LLMs are utilized as travel planning assistants.
• Score: 6.307444995285539
• License:
• Abstract: While large language models (LLMs) show promise in hospitality and tourism, their ability to provide unbiased service across demographic groups remains unclear. This paper explores gender and ethnic biases when LLMs are utilized as travel planning assistants. To investigate this issue, we apply machine learning techniques to analyze travel suggestions generated from three open-source LLMs. Our findings reveal that the performance of race and gender classifiers substantially exceeds random chance, indicating differences in how LLMs engage with varied subgroups. Specifically, outputs align with cultural expectations tied to certain races and genders. To minimize the effect of these stereotypes, we used a stop-word classification strategy, which decreased identifiable differences, with no disrespectful terms found. However, hallucinations related to African American and gender minority groups were noted. In conclusion, while LLMs can generate travel plans seemingly free from bias, it remains essential to verify the accuracy and appropriateness of their recommendations.

Related papers

• Hate Personified: Investigating the role of LLMs in content moderation [64.26243779985393]
  For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected.
  arXiv  Detail & Related papers  (2024-10-03T16:43:17Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases. GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs [58.27353205269664]
  Social biases can manifest in language agency. We introduce the novel Language Agency Bias Evaluation benchmark. We unveil language agency social biases in 3 recent Large Language Model (LLM)-generated content.
  arXiv  Detail & Related papers  (2024-04-16T12:27:54Z)
• Disclosure and Mitigation of Gender Bias in LLMs [64.79319733514266]
  Large Language Models (LLMs) can generate biased responses. We propose an indirect probing framework based on conditional generation. We explore three distinct strategies to disclose explicit and implicit gender bias in LLMs.
  arXiv  Detail & Related papers  (2024-02-17T04:48:55Z)
• Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting [87.30837365008931]
  Large language models (LLMs) equipped with Chain-of-Thought (CoT) prompting are able to make accurate incremental predictions even on unscalable tasks. This study examines the impact of LLMs' step-by-step predictions on gender bias in unscalable tasks.
  arXiv  Detail & Related papers  (2024-01-28T06:50:10Z)
• Aligning with Whom? Large Language Models Have Gender and Racial Biases in Subjective NLP Tasks [15.015148115215315]
  We conduct experiments on four popular large language models (LLMs) to investigate their capability to understand group differences and potential biases in their predictions for politeness and offensiveness. We find that for both tasks, model predictions are closer to the labels from White and female participants. More specifically, when being prompted to respond from the perspective of "Black" and "Asian" individuals, models show lower performance in predicting both overall scores as well as the scores from corresponding groups.
  arXiv  Detail & Related papers  (2023-11-16T10:02:24Z)
• Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation [64.79319733514266]
  Large Language Models (LLMs) can generate biased and toxic responses. We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
  arXiv  Detail & Related papers  (2023-11-01T05:31:46Z)
• Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models [0.0]
  This paper investigates bias along less-studied but still consequential, dimensions, such as age and beauty. We ask whether LLMs hold wide-reaching biases of positive or negative sentiment for specific social groups similar to the "what is beautiful is good" bias found in people in experimental psychology.
  arXiv  Detail & Related papers  (2023-09-16T07:07:04Z)
• Gender bias and stereotypes in Large Language Models [0.6882042556551611]
  This paper investigates Large Language Models' behavior with respect to gender stereotypes. We use a simple paradigm to test the presence of gender bias, building on but differing from WinoBias. Our contributions in this paper are as follows: (a) LLMs are 3-6 times more likely to choose an occupation that stereotypically aligns with a person's gender; (b) these choices align with people's perceptions better than with the ground truth as reflected in official job statistics; (d) LLMs ignore crucial ambiguities in sentence structure 95% of the time in our study items, but when explicitly prompted, they recognize
  arXiv  Detail & Related papers  (2023-08-28T22:32:05Z)
• The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations [5.898806397015801]
  We propose a simple method for analyzing and comparing demographic bias in Large Language Models (LLMs) We demonstrate the effectiveness of our method by measuring intersectional biases within ChatGPT and LLaMA. We identify distinct biases in both models toward various demographic identities, such as both models consistently suggesting low-paying jobs for Mexican workers.
  arXiv  Detail & Related papers  (2023-08-03T21:12:54Z)
• Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models [33.157279170602784]
  We present Marked Personas, a prompt-based method to measure stereotypes in large language models (LLMs) We find that portrayals generated by GPT-3.5 and GPT-4 contain higher rates of racial stereotypes than human-written portrayals using the same prompts. An intersectional lens reveals tropes that dominate portrayals of marginalized groups, such as tropicalism and the hypersexualization of minoritized women.
  arXiv  Detail & Related papers  (2023-05-29T16:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.