💬 NLP

Forgive or forget: Understanding the context of hate in audio retrieval systems

This paper proposes a model-agnostic, post hoc causal debiasing framework called "Forgive or Forget" that utilizes a sentiment-controlled mediator to effectively suppress toxic audio retrievals in text-to-audio systems while preserving semantic relevance and minimizing accuracy loss.

Original authors: Arghya Pal, Sailaja Rajanala, Raphael C. -W. Phan, Shekhar Nayak

Published 2026-06-05

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Arghya Pal, Sailaja Rajanala, Raphael C. -W. Phan, Shekhar Nayak

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a super-smart librarian who can find any sound in the world just by you describing it in words. You say, "I want to hear a man arguing with a woman," and the librarian pulls up the perfect audio clip. This is the promise of Text-to-Audio Retrieval.

However, there's a problem. Because the librarian learned from the entire internet, they sometimes accidentally pull up clips that contain hate speech, insults, or bullying, even when you didn't ask for them. It's like asking for a story about a "fight" and getting a video of a violent hate crime instead of a movie scene.

This paper introduces a new way to teach this librarian to be safer without making them forget how to find the right sounds. The authors call their solution "Forgive or Forget."

Here is how it works, using simple analogies:

The Two-Step Safety Net

The researchers built a two-part system to clean up the results. Think of it as a Security Guard (Forget) and a Quality Inspector (Forgive).

1. The "Forget" Strategy (The Security Guard)

The Problem: Sometimes the librarian gets confused because the words you use can be twisted into something mean.
The Solution: Before the librarian even looks for the sound, this strategy asks a smart AI to imagine "what if" versions of your request that are mean.

The Analogy: Imagine you ask for a picture of a "dog." The "Forget" guard secretly asks the AI to also imagine "a dog barking insults" or "a dog being cruel." It then looks at all these different versions together.
How it helps: If the librarian keeps picking the same bad sound whenever the request is twisted into something mean, the guard realizes, "Ah, this sound is only showing up because of the mean twist, not because it's actually what you wanted." It then lowers the ranking of those sounds. It essentially teaches the system to "forget" the toxic associations so they don't pop up by accident.

2. The "Forgive" Strategy (The Quality Inspector)

The Problem: Sometimes the librarian finds a sound that is almost right, but the audio itself contains a slur or a threat that wasn't in your text description.
The Solution: This strategy waits until the librarian has picked the top 10 or 20 sounds. Then, it listens to them.

The Analogy: Imagine the librarian hands you a stack of 10 photos. The "Forgive" inspector picks up each photo, reads the caption written on the back (transcribing the audio), and checks if it's safe.
How it helps: If the inspector hears a nasty word in the audio, they quietly push that photo to the bottom of the stack and let the next best, safe photo take its place. Crucially, they are "forgiving" of borderline cases. If a clip is an argument but doesn't use hate speech, they let it stay. They only kick out the truly toxic ones.

The Results: Safer Without Losing Quality

The authors tested this on two big libraries of sounds (called AUDIOCAPS and CLOTHO) using three different smart librarian models.

Before the fix: The librarians were finding toxic sounds about 40% to 60% of the time when looking for the top results.
After the fix: By using both "Forget" and "Forgive" together, they reduced toxic results to almost zero (Success Rates jumped to 90%+).
Did they lose the good stuff? No. The paper claims that while they removed the hate, they kept the relevance. The "inspector" didn't throw away good clips just because they were loud or emotional; they only removed the ones with actual hate.

The "Magic" Behind the Scenes

The paper uses some fancy math called Causal Debiasing.

Simple Explanation: Imagine you are trying to figure out if a specific sound is good. But there's a hidden "gremlin" (confounder) messing with the results. The researchers use a "Front-Door" trick. They introduce a middleman (the toxic variants) to see exactly how the "gremlin" is changing the results. By watching how the results change when they add "toxic flavor" to the request, they can mathematically subtract that bad flavor from the final answer.

Summary

The paper proposes a way to make AI sound-search engines safer.

Forget: Teaches the AI not to associate your request with hate by testing "what if" scenarios.
Forgive: Listens to the final results and filters out the bad audio while keeping the good, safe audio.

The result is a system that finds the sounds you want but leaves the hate speech behind, making the technology safer for everyone without making it less useful.

Technical Summary: Forgive or Forget: Understanding the Context of Hate in Audio Retrieval Systems

Problem Statement
Text-to-audio retrieval systems, driven by large-scale audio encoders and transformer-based language models, have achieved significant performance on benchmarks like AUDIOCAPS and CLOTHO. However, these systems face a critical, underexplored challenge: the inadvertent retrieval of toxic or hate audio. Large-scale training datasets inevitably contain harmful speech, including profanities, slurs, and bullying. While toxicity detection in text is well-studied, existing mitigation strategies for text-to-audio retrieval—such as rephrasing or summarization—risk altering the user's original intent or omitting crucial semantic details. The core difficulty lies in the contextual dependencies of hate speech, where the same semantic concept can be expressed toxically or safely depending on phrasing.

Methodology: A Post-Hoc Causal Debiasing Framework
The authors propose a model-agnostic, post-hoc causal debiasing framework designed to suppress harmful speech while preserving semantic relevance. The approach utilizes a sentiment-controlled mediator within a front-door adjustment causal structure.

Causal Structure: In standard retrieval, an input prompt $T$ is embedded as $X$ by a model $M$ to determine ranking $Y$ . Since $M$ acts as an unobserved confounder, standard backdoor adjustments are insufficient. The authors introduce a mediator $Z$ , representing stylistic variants of $X$ infused with six specific toxic intents: Hate, Profanities, Pornographic Language, Slurs, Violence, and Bullying. This creates a mediated path $X \to Z \to Y$ , allowing the system to observe and suppress toxicity-driven biases.

The framework operates via two complementary strategies:

Forget (Model-Level Suppression):
- Mechanism: This strategy generates counterfactual toxic variants of the input query using a Large Language Model (LLM). It applies logit-averaging (inspired by the Noise2Noise principle) across the original prompt and its toxic variants.
- Logic: Toxic biases fluctuate across different stylistic variants, whereas semantic relevance remains stable. By averaging the retrieval logits ( $\ell_f = \frac{1}{|L|} \sum \ell^{(i)}_Y$ ), the system systematically cancels out toxic signals while retaining the core meaning. This prevents audio clips that rank highly solely due to toxic phrasing from being retrieved.
Forgive (Audio-Level Filtering):
- Mechanism: This strategy operates on the retrieved audio candidates. Retrieved clips are first transcribed using a lightweight Speech-to-Text model (Silero ASR) and then classified for toxicity using Detoxify.
- Logic: Toxic audio clips within the Top- $k$ candidates are demoted via softmax renormalization. This approach is "lenient" in that it filters explicit hate speech but retains "borderline" content that is safe, ensuring that useful but sensitive audio is not entirely discarded.

Key Contributions

Novel Framework: Introduction of a post-processing framework integrating causal front-door adjustment with a sentiment-controlled mediator for audio retrieval.
Dual-Strategy Approach: The development of "Forget" (counterfactual prompt generation and logit averaging) and "Forgive" (ASR-based transcription and re-ranking), which address toxicity at both the model and audio levels.
New Evaluation Metrics: Proposal of three specific metrics to evaluate non-toxic retrieval:
- Success Rate: The proportion of retrieved audio that is non-toxic.
- Accuracy: How well non-toxic retrieved audio aligns with the target class.
- Sensitivity: The proportion of correctly retrieved non-toxic audio within the total occurrences of a class.
Model Agnosticism: The framework is designed to integrate seamlessly with existing retrieval pipelines (tested on ATNLL, TUAR, and WavCaps).

Experimental Results
Experiments were conducted on the AUDIOCAPS and CLOTHO datasets using three state-of-the-art models. The results demonstrate:

Toxicity Reduction: The combined "Forget + Forgive" approach consistently yields the highest Success Rate, significantly outperforming baselines. For instance, on AUDIOCAPS at Top@10, the Success Rate for ATNLL improved from 0.60 (baseline) to 0.92 with the combined method.
Preservation of Relevance: Despite aggressive toxicity filtering, the framework maintains high Accuracy and Sensitivity. The combined approach often achieves the best overall Accuracy, indicating that semantic relevance is preserved.
Robustness: Audio quality analysis using Spearman's rank correlation, Pearson's correlation, and Non-Matching Audio Distance (NOMAD) shows that mitigated audios retain over 90% similarity to ground truth, indicating no perceptible degradation in audio quality.
Ablation: "Forget" is identified as the primary driver for toxicity suppression (Success Rate), while "Forgive" contributes to higher Accuracy and Sensitivity by preserving borderline safe content.

Significance and Claims
The paper claims that handling sensitive topics like hate speech in text-to-audio retrieval requires more than simple filtering; it demands an understanding of contextual dependencies. The proposed framework offers a solution that reduces toxic retrieval substantially without compromising the utility of the system. By combining strict systemic suppression ("Forget") with fine-grained audio-level filtering ("Forgive"), the authors achieve state-of-the-art safe text-to-audio retrieval. The work emphasizes that safety and reliability can be improved simultaneously, addressing a critical gap in current multimodal retrieval systems without requiring retraining of the underlying models.