## 2 Related Work
LLMs, like humans, tend to favor additive over subtractive changes when solving problems or enhancing content, which can lead to unnecessarily complex or inefficient solutions [22]. This addition bias aligns with the tendency of models to favor longer outputs. The digit distribution in the LLM pretraining corpus can also induce a bias in digit generation, leading to \"numerical hallucinations\" and overgeneration of smaller digits [23]. These biases can manifest themselves in a variety of ways [1; 8]. LLMs embedded in recommender systems can prioritize recent and popular content, reinforcing existing user preferences and limiting exposure to diverse options, which can negatively affect sales and user satisfaction [8]. Bias can appear locally or globally in text generation systems [1]. Local bias is a property of word-context associations (e.g., the probability of the following token for \"The man was known by...\" vs. \"The woman was known by...\") while global bias is a property of an entire text fragment, such as the sentiment of several generated sentences. As for machine translation systems, gender bias persists, especially when translating between languages with natural gender (such as English) and without gender (such as Persian, Indonesian, or Finnish), with models favoring male pronouns in professional contexts [24]. Detecting and quantifying bias is a critical and challenging research area in information management [1; 8]. Several methodologies have been established, many of which build on and evolve from each other, reflecting the complexity and need for a multifaceted approach. These methodologies are mainly organized around metrics and datasets. Evaluation metrics can be classified according to the level at which they operate: embeddings, probabilities or generated text. Embeddings-based metrics use dense vector representations to measure bias by calculating conceptual distances between target words (e.g., nationalities) and attributes (e.g., races). They include the Word Embedding Association Test (WEAT) [25] and the Sentence Encoder Association Test (SEAT) [26]. As a main drawback, these metrics have a weak or inconsistent relationship with biases in subsequent tasks [1]. Probability-based metrics measure systematic deviations from unbiased behavior using the token probabilities assigned by the model (e.g., masked token methods such as DisCo, LPBS, CBS; pseudo-log-likelihood methods such as CrowS-Pairs Score, CAT, AUL, LMB) [8]. Like embeddings-based metrics, they can also have weak correlations with biases in downstream tasks and are often based on simplified notions of bias, such as binary groups [1]. Generated text-based metrics analyze model-generated text from a prompt (e.g., distribution metrics such as social group substitutions, co-occurrence bias score, demographic representation, or stereotypical associations) [1]. Classifier metrics (such as perspective API, score parity, counterfactual sentiment bias, and regard score) rely on an auxiliary model to score toxicity, sentiment or any other bias dimension of the generated text. Bias is detected if text generated from similar prompts, but with different social groups, is classified differently (e.g., using the Perspective API to detect toxicity) [1]. Lexicon metrics such as HONEST [27] compare each word in the output to a pre-compiled list of words (e.g., derogatory language) to generate a bias score. There are different datasets that have been generated with the main objective of bias assessment, mainly in English, although some are multilingual [1]. They can be mainly classified according to the application tasks. For masked token tasks (where the LLM predicts the most likely word), the most relevant ones include Winogender [28], WinoBias [29], WinoBias+ [30], GAP [31], GAP-Subjective [32], BUG [33], StereoSet [34], or BEC-Pro [35].