Text generation models like GPT-3 can produce remarkably human-like writing. But controlling the emotional tone and feel of generated text requires carefully adjusting model parameters. Here's an overview of how key parameters affect output style and how to tune them for emotive impact.
Temperature
This parameter controls the creativity and randomness of generated text. Lower temperatures (around 0.5) make outputs more rigid and deterministic. Higher temperatures (>1.0) make the text more freewheeling and whimsical. Set the temperature based on whether you want conventional or unorthodox phrasing.
For emotional texts, try higher temperatures - around 0.8 to 1.0 - to encourage more expressive word choices. This allows more variability instead of default logical responses.
Top-p/Top-k
These parameters control diversity by limiting the set of possible next words. Lower values lead to more predictable and repetitive text, while higher values enable more new word combinations.
For emotional text, higher top-p around 0.9 works well to increase variety and uniqueness. This avoids repetitive phrasing that sounds robotic.
Presence Penalty
This adjusts how often the model repeats ideas or words. Higher values discourage repetition, lower values allow more repetition.
Set this lower (around 0.3) for emotional text to allow repetitive phrasing that captures emphatic human expression like "I'm so, so sorry this happened!"
Frequency Penalty
This penalizes new or rare words to make outputs more conventional. Lower this (around 0.5) to encourage more uncommon words that may carry emotional connotations.
Repetition Penalty:
This parameter controls how much the model is penalized for repeating the same phrase or sentence multiple times. Higher values will discourage repetition, while lower values allow for more repetition.
For emotional text, you may want to reduce the repetition penalty (e.g. 1.2) to allow for phrases to be repeated for emphasis ("I'm so so sorry this happened. I'm just so sorry").
Presence penalty and repetition penalty are actually different parameters that serve related but distinct purposes in text generation. Presence penalty reduces repetition of specific words/short phrases, while Repetition penalty reduces repetition of longer sequences/ sentences. They work together to allow fine tuning of how much textual repetition is allowed in generated outputs.
Num Beams:
This controls how many different candidate generations the model samples from at each step. Higher beam sizes lead to more diversity, lower beam sizes reduce variety.
For emotional text, smaller beam sizes like 3-5 can be useful to reduce randomness and keep the text more focused on the target emotion. Too high of a beam size may lead to tonal inconsistencies.
Tuning repetition penalty and beams complementary to temperature, top-p and presence penalty gives fine-grained control over the coherence, repetitiveness and diversity of generated text. Combining all these dials helps steer output towards the desired emotive tone and style.
Together these parameters allow customizing text generation for the right emotional tone - whether passionate, sympathetic, resolute or optimistic. The key is experimenting to understand each parameter's impact. With tuning, AI can converse not just rationally but emotionally too.
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