Gender bias and stereotypes in Large Language Models

Large Language Models (LLMs) have made substantial progress in the past several months, shattering state-of-the-art benchmarks in many domains. This paper investigates LLMs' behavior with respect to gender stereotypes, a known issue for prior models. We use a simple paradigm to test the presence of gender bias, building on but differing from WinoBias, a commonly used gender bias dataset, which is likely to be included in the training data of current LLMs. We test four recently published LLMs and demonstrate that they express biased assumptions about men and women's occupations. 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; (c) LLMs in fact amplify the bias beyond what is reflected in perceptions or the ground truth; (d) LLMs ignore crucial ambiguities in sentence structure 95% of the time in our study items, but when explicitly prompted, they recognize the ambiguity; (e) LLMs provide explanations for their choices that are factually inaccurate and likely obscure the true reason behind their predictions. That is, they provide rationalizations of their biased behavior. This highlights a key property of these models: LLMs are trained on imbalanced datasets; as such, even with the recent successes of reinforcement learning with human feedback, they tend to reflect those imbalances back at us. As with other types of societal biases, we suggest that LLMs must be carefully tested to ensure that they treat minoritized individuals and communities equitably.Comment: ACM Collective Intelligenc

Gender representation in linguistic example sentences

Prior studies have shown that example sentences in syntax textbooks systematically under-represent women and perpetuate gender stereotypes (Macaulay & Brice 1994, 1997; Pabst et al. 2018). We examine the articles published over the past 20 years in Language, Linguistic Inquiry, and Natural Language & Linguistic Theory, and find striking similarities to this prior work. Among our findings, we show a stark imbalance of male (N=10807) to female (N=5019) arguments, and that male-gendered arguments are more likely to be subjects, and female arguments non-subjects. We show that female-gendered arguments are less likely to be referred to using pronouns and are more likely to be referred to using a kinship term, whereas male-gendered arguments are more likely to have occupations and to perpetrate violence. We show that this pattern has remained stable, with very little change, over the course of the twenty years that we examine, leading up to the present day. We conclude with a brief discussion of possible remedies and suggestions for improvement

Pama-Nyungan grandparent systems change with grandchildren, but not cross-cousin terms or social norms

Kinship is a fundamental and universal aspect of the structure of human society. The kinship category of ‘grandparents’ is socially salient, due to grandparents’ investment in the care of the grandchildren as well as to older generations’ control of wealth and cultural knowledge, but the evolutionary dynamics of grandparent terms has yet to be studied in a phylogenetically explicit context. Here, we present the first phylogenetic comparative study of grandparent terms by investigating 134 languages in Pama-Nyungan, an Australian family of hunter-gatherer languages. We infer that proto-Pama-Nyungan had, with high certainty, four separate terms for grandparents. This state then shifted into either a two-term system that distinguishes the genders of the grandparents or a three-term system that merges the ‘parallel’ grandparents, which could then transition into a different three-term system that merges the ‘cross’ grandparents. We find no support for the co-evolution of these systems with either community marriage organisation or post-marital residence. We find some evidence for the correlation of grandparent and grandchild terms, but no support for the correlation of grandparent and cross-cousin terms, suggesting that grandparents and grandchildren potentially form a single lexical category but that the entire kinship system does not necessarily change synchronously

Text-Speech Alignment: A Robin Hood Approach for Endangered Languages

Forced alignment automatically aligns audio recordings of spoken language with transcripts at the level of individual sounds, greatly reducing the time required to prepare data for linguistic analysis. However, existing algorithms are mostly trained on a few well-documented languages. We test the performance of three algorithms against manually aligned data on data from a highly endangered language. At least some tasks, unsupervised alignment (either based on English or trained from a small corpus) is sufficiently reliable for it to be used on legacy data for low-resource languages. Descriptive phonetic work on vowel inventories and prosody can be accurately captured by automatic alignment with minimal training data. Underutilized legacy data exist for many endangered languages. This creates both a need and an opportunity to leverage new technology

Determination of 24 primary aromatic amines in aqueous food simulants by combining solid phase extraction and salting-out assisted liquid?liquid extraction with liquid chromatography tandem mass spectrometry

Carcinogenic primary aromatic amines (PAAs) can be released from improperly manufactured food packaging materials. The limit for the sum of PAAs is set to 10 ?gkg- 1 in Commission Regulation No. 10/2011 (FCM Regulation). However, a lower individual limit, 2 ?gkg- 1 has been recently introduced for the carcinogenic PAAs in Commission Regulation No. 2020/1245. As the majority of the previously published methods are no longer compliant with the current regulation, a UHPLC-MS/MS method was developed to enable food packaging compliance testing for PAAs not only from 3% (w/v) acetic acid, but also from 10% (v/v) ethanol food simulant. Since the latest amendment of the FCM Regulation refers to the list of the 22 restricted PAAs of EU Regulation No. 1907/2006, these PAAs were selected as target compounds along with aniline and p-toluidine, the most common impurities of azo colorants and isocyanates. An enrichment factor of 20 could be achieved combining solid phase extraction with salting-out assisted liquid?liquid extraction. The method was successfully validated and applied on real samples. Limit of quantitation (LOQ) and limit of detection (LOD) values were 0.15 ?gL-1 and 0.05 ?gL-1 for both food simulants, respectively; except for 2,4-diaminotoluene, aniline and 4,4?-oxydianiline. However, even these compounds had lower LOD values than the new individual limit of 2 ?gkg- 1. Cumulative LOD values for both food simulants (1.6 ?gL-1 and 1.5 ?gL-1 for 3% (w/v) acetic acid and 10% (v/v) ethanol, respectively) were lower than the 10 ?gkg- 1 specified in the FCM Regulation. Accuracy values were between 70 and 118% for both food simulants for the majority of PAAs. Both within-day and between-day precision values were below 20%. This method proved to be suitable for daily routine analysis enabling compliance testing of food packaging materials according to the latest regulations. The method was successfully applied for the analysis of plastic kitchenware samples

A Robin Hood approach to forced alignment: English-trained algorithms and their use on Australian languages

Forced alignment automatically aligns audio recordings of spoken language with transcripts at the segment level, greatly reducing the time required to prepare data for phonetic analysis. However, existing algorithms are mostly trained on a few well-documented languages. We test the performance of three algorithms against manually aligned data. For at least some tasks, unsupervised alignment (either based on English or trained from a small corpus) is sufficiently reliable for it to be used on legacy data for low-resource languages. Descriptive phonetic work on vowel inventories and prosody can be accurately captured by automatic alignment with minimal training data. Consonants provided significantly more challenges for forced alignment

Micromechanical Properties of Injection-Molded Starch–Wood Particle Composites

The micromechanical properties of injection molded starch–wood particle composites were investigated as a function of particle content and humidity conditions. The composite materials were characterized by scanning electron microscopy and X-ray diffraction methods. The microhardness of the composites was shown to increase notably with the concentration of the wood particles. In addition,creep behavior under the indenter and temperature dependence were evaluated in terms of the independent contribution of the starch matrix and the wood microparticles to the hardness value. The influence of drying time on the density and weight uptake of the injection-molded composites was highlighted. The results revealed the role of the mechanism of water evaporation, showing that the dependence of water uptake and temperature was greater for the starch–wood composites than for the pure starch sample. Experiments performed during the drying process at 70°C indicated that the wood in the starch composites did not prevent water loss from the samples.Peer reviewe